(3 points)Control charts for individual measurements and wowing ranges were used to monitor theperformance of a chemical process over time. The process was found in a state ofstatistical control at:*=80 and, MR-1.2limits for this process.Assuming that the measurements are normally distributed, find the natural tolerance

Step 1: Understand the given informationWe are given:xˉ=80 (the process mean)MR=1.2 (the average…

Answered: (3 points) Control charts for…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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1.8 A Canadian manufacturer identified a critical diameter on a crank bore that needed to be maintained within a close tolerance for the product to be successful. Sam- ples of size 4 were taken every hour. The values of the differences (measurement - specification), in ten- thousandths of an inch, are given in Table 1.4. (a) Calculate the central line for an X-bar chart for the 24 hourly sample means. The centerline is x = — . . . (4.25 -3.00 -1.50 +3.25)/24. = (b) Is the average of all the numbers in the table, 4 for each hour, the same as the average of the 24 hourly averages? Should it be? (c) A computer calculation gives the control limits LCL = -4.48 UCL 7.88 = Construct the X-bar chart. Identify hours where the process was out of control.
Laetisaric acid is a compound that holds promise for control of fungus diseases in crop plants. The accompanying data show the results of growing the fungus Pythium (y) in various concentrations of laetisaric acid (x). Laetisaric acid concentration (uG/mL) Fungus growth (mm) 0. 33.3 31.0 29.8 27.8 6. 28.0 6. 29.0 10 25.5 10 23.8 20 18.3 20 15.5 30 11.7 30 10.0 Mean 11.500 23.642 Standard deviation 10.884 7.8471 T =-0.98754 %3D a. State the linear regression equation, and with a 0.01 level of significance, predict the amount (in mm) of fungus growth when 25 uG/mL laetisaric acid is applied. Assume the pairs of data follow a bivariate normal distribution and that the scatterplot shows no evidence of a non-linear relationship in the data. b. Determine the percentage of the variation in fungus growth that is explained by the linear relationship between laetisaric acid concentration and fungus growth. Attack Eil
1. Model 1: OLS, using observations 1-706 Dependent variable: RST Coefficient Std. Error t-ratio p-value const 3586.38 38.9124 92.17 <0.0001 *** TOTWRK −0.150746 0.0167403 −9.005 <0.0001 *** Mean dependent var 3266.356 S.D. dependent var 444.4134 Sum squared resid 1.25e+08 S.E. of regression 421.1357 R-squared 0.103287 Adjusted R-squared 0.102014 F(1, 704) 81.08987 P-value(F) 1.99e-1810538.19 Log-likelihood −5267.096 Akaike criterion 10538.19 Schwarz criterion 10547.31 Hannan-Quinn 10541.71 RSTi =3586.38−0.150746 x TOTWRKi , R2=0.103287,SER=421.1357 (38.9124) (0.0167403) Question? could you please help with this question below. 3) By observing the GRETL output in Part (1) above, provide a detailed explanation of the coefficient of determination. Based on your analysis, is this a good model? Why or why not?
To determine if their 1.33 centimeter galvanized pipes are properly adjusted, Fogarty Manufacturing has decided to use an X-Chart which uses the range to estimate the variability in the sample. Answer How to enter your answer Step 3 of 7: What is the Lower Control Limit? Round your answer to three decimal places. 1 ~ 3 Sample Mean Sample Range 1.33 1.3320 1.29 1.38 1.3300 Period obs1 obs2 obs3 obs4 obs5 1.34 1.29 1.35 2 1.35 1.35 3 1.34 1.35 1.36 1.29 1.29 1.31 1.32 1.33 1.34 1.3360 1.35 1.30 1.3340 1.32 1.38 1.3260 1.37 1.33 1.31 1.31 1.29 1.30 1.35 1.31 1,32 1.34 1.38 8 1.35 1.33 1.31 1.34 1.3180 1.33 1.3300 1.34 1.37 1.28 1.36 9 10 1.32 1.37 1.30 1.37 1.29 1.32 1.30 1.28 1.29 11 1.35 1.36 1.38 12 1.36 1.32 1.31 1.30 1.35 13 1.29 1.35 1.32 1.32 1.37 1.35 1.35 1.34 1.32 1.34 14 15 1.32 1.36 1.29 1.32 1.35 45 Table 67 Control Chart 1.35 1.28 Copy Table Average Values The average of the selected cell(s) is 1.340. Copy Value 1.3300 1.3440 1.3080 1.3400 1.3280 1.3300 1.3400 1.3280 0.06…
PLEASE ANSWER ALL THE PARTS OF QUESTION (THIS IS NOT A HRADED ASSIGNMENT)
Assignment: Analyze Filling Line Problems Using Control Charting Attached Files: File Filling line example for X-bar & control chart.xlsxOpen this document with ReadSpeaker docReader (13.354 KB) Using the provided data, calculate the control limits (X-bar and r-chart, Capability Index, create a control chart, and calculate the percentage of units that are expected to be out of spec (z-value). Show your work A quick way to do the percents part of the filling line problem (you know, the percent underfilled, the percent overfilled, etc.), is to use Excel's norm.dist function rather than looking it up on a printed Z table. Use the norm.dist excel function with the LSL in the first position in the parameter list. Norm.dist gives you the left side return from the Z table. "Excel offers you the NORM.DIST statistical function for working with normal distributions. The NORM.DIST function calculates the probability that variable X falls below or at a specified value"…
A random sample of 17 adult male wolves from the Canadian Northwest Territories gave an average weight x1 = 98.6 pounds with estimated sample standard deviation s1 = 5.6 pounds. Another sample of 26 adult male wolves from Alaska gave an average weight x2 = 88.2 pounds with estimated sample standard deviation s2 = 6.0 pounds. (a) Categorize the problem below according to parameter being estimated, proportion p, mean μ, difference of means μ1 – μ2, or difference of proportions p1 – p2. Then solve the problem. μp μ1 – μ2p1 – p2 (b) Let μ1 represent the population mean weight of adult male wolves from the Northwest Territories, and let μ2 represent the population mean weight of adult male wolves from Alaska. Find a 99% confidence interval for μ1 – μ2. (Use 1 decimal place.) lower limit upper limit (c) Examine the confidence interval and explain what it means in the context of this problem. Does the interval consist of numbers that are all positive? all negative? of…

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